A major challenge for contemporary biology is to progress from the analysis of single components or even entire pathways involved in cellular and developmental processes to the study of higher order regulatory networks. Such a systems level analysis requires the convergence of classical genetics and embryology with more recent genomic and computational methods. The goal of this project is to elaborate an integrated experimental strategy for dissecting the structure and function of transcriptional regulatory networks that direct the expression of batteries of developmentally related genes in particular embryonic cells. Body wall muscle development in the Drosophila embryo will be used as a model system since the cell types involved are well-characterized and since both extensive information and genetic resources are available for individual myogenic pathways in this organism. Three specific aims are proposed. In Aim 1, embryonic myoblasts expressing a fluorescent protein will be purified by flow cytometry from wild-type embryos and from embryos in which myogenesis is variably perturbed, RNA from isolated cells will be subjected to transcriptional profiling using DNA microarrays, co-expressed genes will be predicted from a meta-analysis of the profiling dataset compendium, and expression patterns will be validated at single cell resolution by embryo in situ hybridization. In Aim 2, the DNA binding specificities of myoblast transcription factors (TFs) newly identified in the profiling experiments will be determined using a protein binding microarray technology. The derived TF binding sites[unreadable]along with sequence motifs already known to function in muscle-specific enhancers[unreadable]will be input to a novel computational framework that both predicts candidate cis-regulatory modules (CRMs) and assesses the likelihood that a given combination of TFs regulates a particular set of co-expressed genes. The algorithm to be employed incorporates a statistical consideration of TF binding site clustering, interspecies sequence conservation, relevant gene expression data, and a systematic examination of TF combinatorics. Scans for additional shared sequence motifs will be performed on predicted CRMs, and CRMs will be classified based on overall motif composition such that each class represents a potentially unique transcriptional code. In Aim 3, representative examples of each motif and members of each CRM class will be tested for transcriptional activity in transgenic reporter assays. In addition, both appropriate mutants and a rapid, whole embryo RNA interference assay will be used to selectively perturb the derived network in order to assess the functions of novel regulatory components. Collectively, these studies integrate computational and empirical approaches in a manner that establishes a comprehensive experimental paradigm with broad applicability to many developmental systems. Moreover, these investigations provide a knowledge base that will both advance a molecular understanding of human birth defects and inform rational approaches to tissue engineering as a therapeutic modality.